Book description
Create scalable machine learning applications to power a modern data-driven business using Spark 2.x
About This Book
- Get to the grips with the latest version of Apache Spark
- Utilize Spark's machine learning library to implement predictive analytics
- Leverage Spark's powerful tools to load, analyze, clean, and transform your data
Who This Book Is For
If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages.
What You Will Learn
- Get hands-on with the latest version of Spark ML
- Create your first Spark program with Scala and Python
- Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2
- Access public machine learning datasets and use Spark to load, process, clean, and transform data
- Use Spark's machine learning library to implement programs by utilizing well-known machine learning models
- Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models
- Write Spark functions to evaluate the performance of your machine learning models
In Detail
This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.
Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.
By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Style and approach
This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.
Publisher resources
Table of contents
- Preface
-
Getting Up and Running with Spark
- Installing and setting up Spark locally
- Spark clusters
- The Spark programming model
- SchemaRDD
- Spark data frame
- The first step to a Spark program in Scala
- The first step to a Spark program in Java
- The first step to a Spark program in Python
- The first step to a Spark program in R
- Getting Spark running on Amazon EC2
- Configuring and running Spark on Amazon Elastic Map Reduce
- UI in Spark
- Supported machine learning algorithms by Spark
- Benefits of using Spark ML as compared to existing libraries
- Spark Cluster on Google Compute Engine - DataProc
- Summary
- Math for Machine Learning
-
Designing a Machine Learning System
- What is Machine Learning?
- Introducing MovieStream
- Business use cases for a machine learning system
- Types of machine learning models
- The components of a data-driven machine learning system
- An architecture for a machine learning system
- Spark MLlib
- Performance improvements in Spark ML over Spark MLlib
- Comparing algorithms supported by MLlib
- MLlib supported methods and developer APIs
- MLlib vision
- MLlib versions compared
- Summary
- Obtaining, Processing, and Preparing Data with Spark
- Building a Recommendation Engine with Spark
- Building a Classification Model with Spark
-
Building a Regression Model with Spark
- Types of regression models
- Evaluating the performance of regression models
- Extracting the right features from your data
- Training and using regression models
-
Improving model performance and tuning parameters
- Transforming the target variable
- Tuning model parameters
- Summary
- Building a Clustering Model with Spark
- Dimensionality Reduction with Spark
-
Advanced Text Processing with Spark
- What's so special about text data?
- Extracting the right features from your data
- Using a tf-idf model
- Evaluating the impact of text processing
- Text classification with Spark 2.0
- Word2Vec models
- Word2Vec with Spark ML on the 20 Newsgroups dataset
- Summary
-
Real-Time Machine Learning with Spark Streaming
- Online learning
- Stream processing
- Online learning with Spark Streaming
- Online model evaluation
- Structured Streaming
- Summary
- Pipeline APIs for Spark ML
Product information
- Title: Machine Learning with Spark - Second Edition
- Author(s):
- Release date: April 2017
- Publisher(s): Packt Publishing
- ISBN: 9781785889936
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